scholarly journals Visual prototypes in the ventral stream are attuned to complexity and gaze behavior

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Olivia Rose ◽  
James Johnson ◽  
Binxu Wang ◽  
Carlos R. Ponce
Keyword(s):  
Microelectrode Arrays ◽  
Semantic Content ◽  
Natural Images ◽  
Gaze Behavior ◽  
Neuronal Responses ◽  
Inferotemporal Cortex ◽  
Efficient Coding ◽  
Other Information ◽  
Cortical Regions ◽  
Moderate Complexity

AbstractEarly theories of efficient coding suggested the visual system could compress the world by learning to represent features where information was concentrated, such as contours. This view was validated by the discovery that neurons in posterior visual cortex respond to edges and curvature. Still, it remains unclear what other information-rich features are encoded by neurons in more anterior cortical regions (e.g., inferotemporal cortex). Here, we use a generative deep neural network to synthesize images guided by neuronal responses from across the visuocortical hierarchy, using floating microelectrode arrays in areas V1, V4 and inferotemporal cortex of two macaque monkeys. We hypothesize these images (“prototypes”) represent such predicted information-rich features. Prototypes vary across areas, show moderate complexity, and resemble salient visual attributes and semantic content of natural images, as indicated by the animals’ gaze behavior. This suggests the code for object recognition represents compressed features of behavioral relevance, an underexplored aspect of efficient coding.

fMRI–Adaptation Studies of Viewpoint Tuning in the Extrastriate and Fusiform Body Areas

2010 ◽  
Vol 103 (3) ◽  
pp. 1467-1477 ◽  
Author(s):  
John C. Taylor ◽  
Alison J. Wiggett ◽  
Paul E. Downing
Keyword(s):  
Human Body ◽  
Adaptation Effect ◽  
The Body ◽  
Neural Mechanisms ◽  
Similar Degree ◽  
Neuronal Responses ◽  
Adaptation Studies ◽  
View Invariance ◽  
Cortical Regions ◽  
Fmri Adaptation

People are easily able to perceive the human body across different viewpoints, but the neural mechanisms underpinning this ability are currently unclear. In three experiments, we used functional MRI (fMRI) adaptation to study the view-invariance of representations in two cortical regions that have previously been shown to be sensitive to visual depictions of the human body—the extrastriate and fusiform body areas (EBA and FBA). The BOLD response to sequentially presented pairs of bodies was treated as an index of view invariance. Specifically, we compared trials in which the bodies in each image held identical poses (seen from different views) to trials containing different poses. EBA and FBA adapted to identical views of the same pose, and both showed a progressive rebound from adaptation as a function of the angular difference between views, up to ∼30°. However, these adaptation effects were eliminated when the body stimuli were followed by a pattern mask. Delaying the mask onset increased the response (but not the adaptation effect) in EBA, leaving FBA unaffected. We interpret these masking effects as evidence that view-dependent fMRI adaptation is driven by later waves of neuronal responses in the regions of interest. Finally, in a whole brain analysis, we identified an anterior region of the left inferior temporal sulcus (l-aITS) that responded linearly to stimulus rotation, but showed no selectivity for bodies. Our results show that body-selective cortical areas exhibit a similar degree of view-invariance as other object selective areas—such as the lateral occipitotemporal area (LO) and posterior fusiform gyrus (pFs).

Cerebral Cortex

2018 ◽  
Author(s):  
James A. Anderson
Keyword(s):  
Cerebral Cortex ◽  
Pyramidal Cells ◽  
Single Units ◽  
Local Processing ◽  
Complex Object ◽  
Inferotemporal Cortex ◽  
Imaging Studies ◽  
Cortical Columns ◽  
Cortical Regions ◽  
Intrinsic Imaging

There is important local processing in cortex as well as the more dramatic massive projections back and forth between cortical regions. Using short, slow, local connections eliminates many long, expensive, fast interregional connections. Cortical pyramidal cells connect to neighbors over several millimeters in the form of patchy connections. Connections are often reciprocal between patches. Groups of cells called cortical columns are ubiquitous in cortex and seem to be fundamental architectural units. A functional column is perhaps .3 mm in diameter containing perhaps 10,000 cells. Intrinsic imaging studies of columns in inferotemporal cortex show they respond selectively to complex aspects of images. A small number of columns respond to a complex object. In inferotemporal cortex, these responses might be “words” in a language of vision. There is evidence for scaling of computation from single units to cortical regions. Understanding the function of such ensembles is the future.

scholarly journals Evolving super stimuli for real neurons using deep generative networks

2019 ◽  
Author(s):  
Carlos R. Ponce ◽  
Will Xiao ◽  
Peter F. Schade ◽  
Till S. Hartmann ◽  
Gabriel Kreiman ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Visual Cortex ◽  
Neural Coding ◽  
Deep Neural Network ◽  
Natural Images ◽  
List Type ◽  
Neuronal Responses ◽  
Diagnostic Features ◽  
Neural Selectivity

AbstractFinding the best stimulus for a neuron is challenging because it is impossible to test all possible stimuli. Here we used a vast, unbiased, and diverse hypothesis space encoded by a generative deep neural network model to investigate neuronal selectivity in inferotemporal cortex without making any assumptions about natural features or categories. A genetic algorithm, guided by neuronal responses, searched this space for optimal stimuli. Evolved synthetic images evoked higher firing rates than even the best natural images and revealed diagnostic features, independently of category or feature selection. This approach provides a way to investigate neural selectivity in any modality that can be represented by a neural network and challenges our understanding of neural coding in visual cortex.HighlightsA generative deep neural network interacted with a genetic algorithm to evolve stimuli that maximized the firing of neurons in alert macaque inferotemporal and primary visual cortex.The evolved images activated neurons more strongly than did thousands of natural images.Distance in image space from the evolved images predicted responses of neurons to novel images.

scholarly journals A visual encoding model links magnetoencephalography signals to neural synchrony in human cortex

2020 ◽  
Author(s):  
Eline R. Kupers ◽  
Noah C. Benson ◽  
Jonathan Winawer
Keyword(s):  
Visual Stimulation ◽  
Neural Synchrony ◽  
Systematic Effect ◽  
Neural Responses ◽  
Neuronal Responses ◽  
Spatial Pooling ◽  
Visual Encoding ◽  
Model Predictions ◽  
Cortical Regions ◽  
Spatial Topography

AbstractSynchronization of neuronal responses over large distances is hypothesized to be important for many cortical functions. However, no straightforward methods exist to estimate synchrony non-invasively in the living human brain. MEG and EEG measure the whole brain, but the sensors pool over large, overlapping cortical regions, obscuring the underlying neural synchrony. Here, we developed a model from stimulus to cortex to MEG sensors to disentangle neural synchrony from spatial pooling of the instrument. We find that synchrony across cortex has a surprisingly large and systematic effect on predicted MEG spatial topography. We then conducted visual MEG experiments and separated responses into stimulus-locked and broadband components. The stimulus-locked topography was similar to model predictions assuming synchronous neural sources, whereas the broadband topography was similar to model predictions assuming asynchronous sources. We infer that visual stimulation elicits two distinct types of neural responses, one highly synchronous and one largely asynchronous across cortex.

Size and position invariance of neuronal responses in monkey inferotemporal cortex

1995 ◽  
Vol 73 (1) ◽  
pp. 218-226 ◽  
Author(s):  
M. Ito ◽  
H. Tamura ◽  
I. Fujita ◽  
K. Tanaka
Keyword(s):  
Anterior Part ◽  
Single Cells ◽  
Stimulus Size ◽  
Long Term Memory ◽  
Neuronal Responses ◽  
Inferotemporal Cortex ◽  
Size Dependent ◽  
Neuronal Mechanisms ◽  
Specific Manner

1. Object vision is largely invariant to changes of retinal images of objects in size and position. To reveal neuronal mechanisms of this invariance, we recorded activities from single cells in the anterior part of the inferotemporal cortex (anterior IT), determined the critical features for the activation of individual cells, and examined the effects of changes in stimulus size and position on the responses. 2. Twenty-one percent of the anterior IT cells studied here responded to ranges of size > 4 octaves, whereas 43% responded to size ranges < 2 octaves. The optimal stimulus size, measured by the distance between the outer edges along the longest axis of the stimulus, ranged from 1.7 to 30 degrees. 3. The selectivity for shape was mostly preserved over the entire range of effective size and over the receptive field, whereas some subtle but statistically significant changes were observed in one half of the cells studied here. 4. The size-specific responses observed in 43% of the cells are consistent with recent psychophysical data that suggest that images of objects are stored in a size-specific manner in the long-term memory. Both size-dependent and -independent processing of images may occur in anterior IT.

scholarly journals Effect of silhouetting and inversion on view invariance in the monkey inferotemporal cortex

2017 ◽  
Vol 118 (1) ◽  
pp. 353-362
Author(s):  
N. Apurva Ratan Murty ◽  
S. P. Arun
Keyword(s):  
Object Representations ◽  
Neuronal Responses ◽  
Inferotemporal Cortex ◽  
View Invariance ◽  
Natural Objects ◽  
Viewpoint Invariance ◽  
And Inversion ◽  
The Impact ◽  
The Brain ◽  
Coarse To Fine

We effortlessly recognize objects across changes in viewpoint, but we know relatively little about the features that underlie viewpoint invariance in the brain. Here, we set out to characterize how viewpoint invariance in monkey inferior temporal (IT) neurons is influenced by two image manipulations—silhouetting and inversion. Reducing an object into its silhouette removes internal detail, so this would reveal how much viewpoint invariance depends on the external contours. Inverting an object retains but rearranges features, so this would reveal how much viewpoint invariance depends on the arrangement and orientation of features. Our main findings are 1) view invariance is weakened by silhouetting but not by inversion; 2) view invariance was stronger in neurons that generalized across silhouetting and inversion; 3) neuronal responses to natural objects matched early with that of silhouettes and only later to that of inverted objects, indicative of coarse-to-fine processing; and 4) the impact of silhouetting and inversion depended on object structure. Taken together, our results elucidate the underlying features and dynamics of view-invariant object representations in the brain. NEW & NOTEWORTHY We easily recognize objects across changes in viewpoint, but the underlying features are unknown. Here, we show that view invariance in the monkey inferotemporal cortex is driven mainly by external object contours and is not specialized for object orientation. We also find that the responses to natural objects match with that of their silhouettes early in the response, and with inverted versions later in the response—indicative of a coarse-to-fine processing sequence in the brain.

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